- // Tencent is pleased to support the open source community by making ncnn available.
- //
- // Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
- //
- // Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
- // in compliance with the License. You may obtain a copy of the License at
- //
- // https://opensource.org/licenses/BSD-3-Clause
- //
- // Unless required by applicable law or agreed to in writing, software distributed
- // under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
- // CONDITIONS OF ANY KIND, either express or implied. See the License for the
- // specific language governing permissions and limitations under the License.
-
- #include "testutil.h"
-
- #include "layer/deconvolutiondepthwise.h"
-
- static int test_deconvolutiondepthwise(int w, int h, int c, int outch, int kernel, int dilation, int stride, int pad, int bias, int group)
- {
- ncnn::Mat a = RandomMat(w, h, c);
-
- ncnn::ParamDict pd;
- pd.set(0, outch);// num_output
- pd.set(1, kernel);// kernel_w
- pd.set(2, dilation);// dilation_w
- pd.set(3, stride);// stride_w
- pd.set(4, pad);// pad_w
- pd.set(5, bias);// bias_term
- pd.set(6, outch/group*c/group*kernel*kernel*group);
- pd.set(7, group);
-
- int activation_type = RAND() % 5;// 0 1 2 3 4
- ncnn::Mat activation_params(2);
- activation_params[0] = RandomFloat(-1, 0);// alpha
- activation_params[1] = RandomFloat(0, 1);// beta
- pd.set(9, activation_type);
- pd.set(10, activation_params);
-
- std::vector<ncnn::Mat> weights(2);
- weights[0] = RandomMat(outch/group*c/group*kernel*kernel*group);
- weights[1] = RandomMat(outch);
-
- ncnn::Option opt;
- opt.num_threads = 1;
- opt.use_vulkan_compute = true;
- opt.use_int8_inference = false;
-
- int ret = test_layer<ncnn::DeconvolutionDepthWise>("DeconvolutionDepthWise", pd, weights, opt, a);
- if (ret != 0)
- {
- fprintf(stderr, "test_deconvolutiondepthwise failed w=%d h=%d c=%d outch=%d kernel=%d dilation=%d stride=%d pad=%d bias=%d group=%d act=%d actparams=[%f,%f]\n", w, h, c, outch, kernel, dilation, stride, pad, bias, group, activation_type, activation_params[0], activation_params[1]);
- }
-
- return ret;
- }
-
- static int test_deconvolutiondepthwise_0()
- {
- static const int kdsp[16][4] = {
- {1, 1, 1, 0},
- {1, 1, 2, 0},
- {2, 1, 1, 1},
- {2, 1, 2, 1},
- {3, 1, 1, 1},
- {3, 1, 2, 1},
- {3, 2, 1, 1},
- {4, 1, 1, 2},
- {4, 1, 2, 2},
- {4, 2, 1, 2},
- {5, 1, 1, 2},
- {5, 1, 2, 2},
- {5, 2, 2, 2},
- {7, 1, 1, 3},
- {7, 1, 2, 3},
- {7, 2, 1, 3},
- };
-
- for (int i=0; i<16; i++)
- {
- int ret = 0
- || test_deconvolutiondepthwise(9, 7, 1, 1, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 1)
- || test_deconvolutiondepthwise(9, 7, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 1)
- || test_deconvolutiondepthwise(9, 7, 2, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 2)
- || test_deconvolutiondepthwise(9, 7, 3, 3, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 3)
- || test_deconvolutiondepthwise(9, 7, 4, 2, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 2)
- || test_deconvolutiondepthwise(9, 7, 4, 4, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 4)
- || test_deconvolutiondepthwise(9, 7, 7, 7, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 7)
- || test_deconvolutiondepthwise(9, 7, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 2)
- || test_deconvolutiondepthwise(9, 7, 8, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 8)
- || test_deconvolutiondepthwise(9, 7, 12, 12, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 4)
- || test_deconvolutiondepthwise(9, 7, 15, 15, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 15)
- || test_deconvolutiondepthwise(9, 7, 16, 8, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 0, 2)
- || test_deconvolutiondepthwise(9, 7, 16, 16, kdsp[i][0], kdsp[i][1], kdsp[i][2], kdsp[i][3], 1, 16)
- ;
-
- if (ret != 0)
- return -1;
- }
-
- return 0;
- }
-
- int main()
- {
- SRAND(7767517);
-
- return test_deconvolutiondepthwise_0();
- }
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